code: def create(
      self,
      target: str,
      df: Optional[pd.DataFrame] = None,
      args: Optional[Dict] = None,
  ) -> None:
      if args is None:
          args = {}
  
      if df is not None:
          target_dtype = infer_types(df, 0).to_dict()["dtypes"][target]
          model = AutoML(verbose=0)
  
          if target_dtype in ["binary", "categorical", "tags"]:
              model.fit(
                  X_train=df.drop(columns=[target]),
                  y_train=df[target],
                  task="classification",
                  **args.get("using"),
              )
  
          elif target_dtype in ["integer", "float", "quantity"]:
              model.fit(
                  X_train=df.drop(columns=[target]),
                  y_train=df[target],
                  task="regression",
                  **args.get("using"),
              )
  
          self.model_storage.json_set("args", args)
          self.model_storage.file_set("model", dill.dumps(model))
  
      else:
          raise Exception("Data is empty!!")
  
file_path: mindsdb/mindsdb/integrations/handlers/flaml_handler/flaml_handler.py
imports: import dill
  import pandas
  from mindsdb.integrations.libs.base import BaseMLEngine
  from typing import Dict, Optional
  from type_infer.api import infer_types
  from flaml import AutoML
label_desc: Use image node_id 1 for calling the FLAMLHandler obj's underlying member method code with example usage: obj.create(target, df, args) without return types